ANCAT Analysis: 24-month-olds
Analysis Timestamp:
date()## [1] "Thu Jun 23 18:06:29 2016"Dependencies
library(ggplot2, quietly=T)
library(lme4, quietly=T)
library(psych, quietly=T)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(dplyr, quietly=T)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionRaw data processing…
source('./process_tobii_data_bf.R')
auto_process_tobii('ancat-aoi.txt','ancat-phasetiming.txt')
concat_csv('master.csv')
# fix subject names
master <- read.csv('master.csv')
master$ParticipantName <- gsub('[^a-zA-Z0-9]','',master$ParticipantName)
write.csv(master,'master.csv')
# prepare subjects file
subjects <- read.csv('subjects.csv')
prepare_master(subjectfile = './subjects.csv', trialfile = FALSE)
# create clean file
file.copy('master-prepared.csv','master-clean.csv',overwrite = T)
# exclude exclusions
master_clean <- read.csv('master-clean.csv')
master_clean <- master_clean[which(master_clean$Excluded == 0), ]
# get rid of additional Condition.x and use the Condition.y column from
# the subjects file
master_clean <- master_clean[ , -which(names(master_clean) %in% c("Condition.x"))]
names(master_clean)[names(master_clean)=="Condition.y"] <- "Condition"
# add KnownAnimal column
master_clean$KnownAnimal <- 0
master_clean[which(master_clean$Trial == 'UnfamiliarArmadillo' & master_clean$Armadillo == 1), 'KnownAnimal'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarFlamingo' & master_clean$Flamingo == 1), 'KnownAnimal'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarLizard' & master_clean$Lizard == 1), 'KnownAnimal'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarHedgehog' & master_clean$Hedgehog == 1), 'KnownAnimal'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarLemur' & master_clean$Lemur == 1), 'KnownAnimal'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarRhino' & master_clean$Rhinoceros == 1), 'KnownAnimal'] <- 1
# add KnownVerb column
master_clean$KnownVerb <- 0
master_clean[which(master_clean$Trial == 'UnfamiliarHedgehog' & master_clean$Condition == 'informative' & master_clean$Sleep == 1), 'KnownVerb'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarArmadillo' & master_clean$Condition == 'informative' & master_clean$Look == 1), 'KnownVerb'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarLizard' & master_clean$Condition == 'informative' & master_clean$Cry == 1), 'KnownVerb'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarFlamingo' & master_clean$Condition == 'informative' & master_clean$Dance == 1), 'KnownVerb'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarLemur' & master_clean$Condition == 'informative' & master_clean$Eat == 1), 'KnownVerb'] <- 1
master_clean[which(master_clean$Trial == 'UnfamiliarRhino' & master_clean$Condition == 'informative' & master_clean$Drink == 1), 'KnownVerb'] <- 1
# add Verb column
master_clean$Verb <- ''
master_clean[which(master_clean$Trial == 'UnfamiliarHedgehog'), 'Verb'] <- 'Sleep'
master_clean[which(master_clean$Trial == 'UnfamiliarArmadillo'), 'Verb'] <- 'Look'
master_clean[which(master_clean$Trial == 'UnfamiliarLizard'), 'Verb'] <- 'Cry'
master_clean[which(master_clean$Trial == 'UnfamiliarFlamingo'), 'Verb'] <- 'Dance'
master_clean[which(master_clean$Trial == 'UnfamiliarLemur'), 'Verb'] <- 'Eat'
master_clean[which(master_clean$Trial == 'UnfamiliarRhino'), 'Verb'] <- 'Drink'
# re-name factor levels.
levels(master_clean$Condition) <- c('Informative','Neutral')
# re-factor to get rid of any bad condition names which no longer exist in the dataset
# master_clean$Condition <- factor(as.character(trim(master_clean$Condition)))
#write.csv(master_clean,'master-clean.csv',row.names = FALSE)
save(master_clean,file='master-clean.Rdata')Load and set data options for eyetrackingR library.
#devtools::install_github('jwdink/eyetrackingR')
library(eyetrackingR)
data <- make_eyetrackingr_data(master_clean,
participant_column = "ParticipantName",
trial_column = "Trial",
time_column = "TimeFromPhaseOnset",
trackloss_column = "TrackLoss",
aoi_columns = c('Animate','Inanimate'),
treat_non_aoi_looks_as_missing = TRUE
)## Converting Trackloss to proper type.## Warning in check_then_convert(x, is.logical2, as.logical, "Trackloss"):
## Found NAs in trackloss column, these will be treated as TRACKLOSS=FALSE.## Converting AOI to proper type.## Converting AOI to proper type.# add kind of trial
data$TrialType <- ifelse(grepl('Unfamiliar',data$Trial), 'Unfamiliar','Familiar')
data$TrialType <- factor(data$TrialType)
# add Target for Familiar trials
data$Target <- as.factor( ifelse(test = grepl('(Spoon|Bottle)', data$Trial),
yes = 'Inanimate',
no = 'Animate') )
# convert age
data$Age <- as.numeric(as.character(data$Age))Zoom in to response window
response_window <- subset_by_window(data, window_start_time = 15500, window_end_time = 21000, rezero=TRUE, remove=TRUE)## Avg. window length in new data will be 5500Familiar Trials
familiar_raw <- filter(response_window, TrialType == 'Familiar')Clean by trackloss
trackloss <- trackloss_analysis(data = familiar_raw)
# set a threshold for amount of trackloss that is acceptable
prop_thresh <- mean(trackloss$TracklossForTrial) + 2*sd(trackloss$TracklossForTrial)
familiar <- clean_by_trackloss(data = familiar_raw, trial_prop_thresh = prop_thresh)## Performing Trackloss Analysis...## Will exclude trials whose trackloss proportion is greater than : 0.636088292477149## ...removed 18 trials.trackloss_clean <- trackloss_analysis(data = familiar)
trackloss_clean_subjects <- unique(trackloss_clean[, c('ParticipantName','TracklossForParticipant')])
# get mean samples contributed per trials, with SD
mean(1 - trackloss_clean_subjects$TracklossForParticipant)## [1] 0.828145sd(1- trackloss_clean_subjects$TracklossForParticipant)## [1] 0.1075668# get number of trials contributed by participant
interim_summary <- describe_data(familiar, 'Animate', c('ParticipantName'))
interim_summary## Source: local data frame [48 x 8]
##
## ParticipantName Mean SD Var Min Max N
## <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 ANCAT177 0.5577812 0.4970332 0.2470420 0 1 990
## 2 ANCAT180 0.5454545 0.4981655 0.2481689 0 1 1650
## 3 ANCAT181 0.4929835 0.5001034 0.2501034 0 1 1980
## 4 ANCAT183 0.4592760 0.4986209 0.2486228 0 1 1320
## 5 ANCAT184 0.6544811 0.4756774 0.2262690 0 1 1980
## 6 ANCAT190 0.7213325 0.4484872 0.2011408 0 1 1980
## 7 ANCAT191 0.7262314 0.4460194 0.1989333 0 1 1980
## 8 ANCAT194 0.6402003 0.4801133 0.2305088 0 1 1980
## 9 ANCAT195 0.6763902 0.4680665 0.2190862 0 1 1320
## 10 ANCAT198 0.6895735 0.4628054 0.2141888 0 1 1980
## .. ... ... ... ... ... ... ...
## Variables not shown: NumTrials <int>.# remove participants who have less than 3 trials of either type
subjects_to_remove <- as.character(filter(interim_summary, NumTrials < 3)$ParticipantName)
familiar <- familiar %>%
filter(!(ParticipantName %in% subjects_to_remove))
# final counts:
final_summary <- describe_data(familiar, 'Animate', c('ParticipantName'))
final_summary## Source: local data frame [48 x 8]
##
## ParticipantName Mean SD Var Min Max N
## <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 ANCAT177 0.5577812 0.4970332 0.2470420 0 1 990
## 2 ANCAT180 0.5454545 0.4981655 0.2481689 0 1 1650
## 3 ANCAT181 0.4929835 0.5001034 0.2501034 0 1 1980
## 4 ANCAT183 0.4592760 0.4986209 0.2486228 0 1 1320
## 5 ANCAT184 0.6544811 0.4756774 0.2262690 0 1 1980
## 6 ANCAT190 0.7213325 0.4484872 0.2011408 0 1 1980
## 7 ANCAT191 0.7262314 0.4460194 0.1989333 0 1 1980
## 8 ANCAT194 0.6402003 0.4801133 0.2305088 0 1 1980
## 9 ANCAT195 0.6763902 0.4680665 0.2190862 0 1 1320
## 10 ANCAT198 0.6895735 0.4628054 0.2141888 0 1 1980
## .. ... ... ... ... ... ... ...
## Variables not shown: NumTrials <int>.# how many participants?
length(unique(final_summary$ParticipantName))## [1] 48# how many trials?
mean(final_summary$NumTrials)## [1] 5.625sd(final_summary$NumTrials)## [1] 0.7614376Summarize
(familiar_summary <- familiar %>%
group_by(ParticipantName, Target) %>%
summarise(Animate = mean(Animate, na.rm=TRUE),
NTrials = length(unique(Trial))) %>%
ungroup() %>%
group_by(Target) %>%
summarise(MeanAnimate = mean(Animate),
SDAnimate = sd(Animate),
N = n(),
MeanTrials = mean(NTrials),
SDTrials = sd(NTrials)) %>%
ungroup())## Source: local data frame [2 x 6]
##
## Target MeanAnimate SDAnimate N MeanTrials SDTrials
## <fctr> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Animate 0.7717944 0.1163495 48 3.6875 0.6890095
## 2 Inanimate 0.3706804 0.1511891 48 1.9375 0.2446230subjects <- familiar %>%
distinct(ParticipantName,Age_Group,Age,Sex,Condition) %>%
group_by(Condition,Age_Group) %>%
summarise(N=length(ParticipantName), MeanAge = mean(Age,na.rm=TRUE), N_Males = length(Sex[Sex=='M'])) %>%
ungroup()
subjects## Source: local data frame [2 x 5]
##
## Condition Age_Group N MeanAge N_Males
## <fctr> <fctr> <int> <dbl> <int>
## 1 Informative 24-months 24 24.13667 11
## 2 Neutral 24-months 24 24.22208 12Window Analysis
familiar_summary <- describe_data(familiar, describe_column='Animate', group_columns=c('Target'))
familiar_summary## Source: local data frame [2 x 8]
##
## Target Mean SD Var Min Max N NumTrials
## <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 Animate 0.7706458 0.4204218 0.1767545 0 1 58410 4
## 2 Inanimate 0.3725520 0.4834936 0.2337660 0 1 30690 2# agg by subject
familiar_sub <- make_time_window_data(familiar,
aois='Animate',
predictor_columns=c('Target','Age','MCDI_Total'),
summarize_by = "ParticipantName")
# take a quick peek at data
ggplot(familiar_sub, aes(x=Target,y=Prop,group=ParticipantName)) +
geom_point() +
geom_line()
# simple paired t-test between conditions
t.test(ArcSin ~ Target, data=familiar_sub, paired=TRUE)##
## Paired t-test
##
## data: ArcSin by Target
## t = 12.2, df = 47, p-value = 3.592e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3695234 0.5154568
## sample estimates:
## mean of the differences
## 0.4424901# mixed-effects model
familiar_trial <- make_time_window_data(familiar,
aois='Animate',
predictor_columns=c('Target','Age'))
# sum-code and center predictors
familiar_trial$TargetC <- ifelse(familiar_trial$Target == 'Animate', .5, -.5)
familiar_trial$TargetC <- familiar_trial$TargetC - mean(familiar_trial$TargetC)
# mixed-effects linear model on subject*trial data
model <- lmer(ArcSin ~ TargetC + (1 | Trial) + (1 | ParticipantName), data = familiar_trial, REML = FALSE)
summary(model)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ArcSin ~ TargetC + (1 | Trial) + (1 | ParticipantName)
## Data: familiar_trial
##
## AIC BIC logLik deviance df.resid
## 7.7 25.7 1.2 -2.3 265
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.96076 -0.65849 -0.01642 0.68506 2.41056
##
## Random effects:
## Groups Name Variance Std.Dev.
## ParticipantName (Intercept) 0.004992 0.07066
## Trial (Intercept) 0.008849 0.09407
## Residual 0.051270 0.22643
## Number of obs: 270, groups: ParticipantName, 48; Trial, 6
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.95379 0.04208 22.665
## TargetC 0.47546 0.08649 5.497
##
## Correlation of Fixed Effects:
## (Intr)
## TargetC -0.020drop1(model,~.,test="Chi")## Single term deletions
##
## Model:
## ArcSin ~ TargetC + (1 | Trial) + (1 | ParticipantName)
## Df AIC LRT Pr(Chi)
## <none> 7.6885
## TargetC 1 16.4630 10.775 0.001029 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Growth Curve Analysis
# aggregate across trials within subjects in time analysis
response_time <- make_time_sequence_data(familiar, time_bin_size = 100,
predictor_columns = c("Target"),
aois = "Animate"
)
# visualize time results
plot(response_time, predictor_column = "Target") +
theme_light() +
coord_cartesian(ylim = c(0,1))## Warning: Removed 95 rows containing non-finite values (stat_summary).
## Warning: Removed 95 rows containing non-finite values (stat_summary).
# sum-code and center our predictor:
response_time$TargetC <- ifelse(response_time$Target == 'Animate', .5, -.5)
response_time$TargetC <- response_time$TargetC - mean(response_time$TargetC)
# Construct model
model_time_sequence <- lmer(ArcSin ~ TargetC*(ot1+ot2+ot3+ot4) + (1 + ot1 + ot2 + ot3 + ot4 | Trial) + (1 + ot1 + ot2 + ot3 + ot4 | ParticipantName), data = response_time, REML = FALSE)
summary(model_time_sequence)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ArcSin ~ TargetC * (ot1 + ot2 + ot3 + ot4) + (1 + ot1 + ot2 +
## ot3 + ot4 | Trial) + (1 + ot1 + ot2 + ot3 + ot4 | ParticipantName)
## Data: response_time
##
## AIC BIC logLik deviance df.resid
## 25057.8 25364.6 -12487.9 24975.8 13092
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7614 -0.7711 0.1746 0.6886 2.5880
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantName (Intercept) 0.01856 0.1363
## ot1 0.80430 0.8968 0.44
## ot2 0.54283 0.7368 -0.20 0.13
## ot3 0.53798 0.7335 -0.22 -0.31 -0.35
## ot4 0.38425 0.6199 0.27 -0.09 0.02 0.03
## Trial (Intercept) 0.01192 0.1092
## ot1 0.06848 0.2617 0.42
## ot2 0.02680 0.1637 -0.12 0.00
## ot3 0.07742 0.2783 -0.19 -0.01 -0.94
## ot4 0.15610 0.3951 0.40 -0.46 0.51 -0.71
## Residual 0.37614 0.6133
## Number of obs: 13133, groups: ParticipantName, 48; Trial, 6
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.97887 0.04904 19.960
## TargetC 0.60317 0.09522 6.334
## ot1 -0.57118 0.17313 -3.299
## ot2 0.19319 0.13241 1.459
## ot3 -0.12104 0.16080 -0.753
## ot4 0.04510 0.18908 0.238
## TargetC:ot1 -1.20442 0.24242 -4.968
## TargetC:ot2 -1.12423 0.16551 -6.793
## TargetC:ot3 1.11403 0.25556 4.359
## TargetC:ot4 -0.70774 0.35262 -2.007
##
## Correlation of Fixed Effects:
## (Intr) TargtC ot1 ot2 ot3 ot4 TrgC:1 TrgC:2 TrgC:3
## TargetC -0.021
## ot1 0.372 -0.005
## ot2 -0.116 0.002 0.088
## ot3 -0.183 0.003 -0.157 -0.516
## ot4 0.362 -0.008 -0.275 0.228 -0.413
## TargetC:ot1 -0.007 0.397 -0.010 0.003 0.002 0.008
## TargetC:ot2 0.003 -0.097 0.003 -0.004 0.016 -0.007 0.014
## TargetC:ot3 0.004 -0.182 0.001 0.012 -0.013 0.015 -0.009 -0.751
## TargetC:ot4 -0.008 0.385 0.006 -0.005 0.013 -0.018 -0.420 0.425 -0.645plot(response_time, predictor_column = "Target", dv = "ArcSin", model = model_time_sequence) + theme_light()## Warning: Removed 95 rows containing non-finite values (stat_summary).
## Warning: Removed 95 rows containing non-finite values (stat_summary).
Bootstrapped Spline Analysis
response_familiar_time <- make_time_sequence_data(familiar,
time_bin_size = 10,
predictor_columns = c("Target"),
aois = "Animate",
summarize_by = "ParticipantName"
)
# visualize timecourse
plot(response_familiar_time, predictor_column = "Target") +
theme_light() +
coord_cartesian(ylim = c(0,1))## Warning: Removed 1046 rows containing non-finite values (stat_summary).
## Warning: Removed 1046 rows containing non-finite values (stat_summary).
# bootstrap with smoothing splines
bootstrapped_familiar <- make_boot_splines_data(response_familiar_time,
predictor_column = 'Target',
within_subj = TRUE,
bs_samples = 5000,
alpha = .05,
smoother = "smooth.spline")## Warning: Calling boot-splines from this function is deprecated. Please use
## `analyze_time_bins` for boot-splines.plot(bootstrapped_familiar)## Plotting within-subjects differences...
# show us the divergences where p < .05
bootstrap_analysis_familiar <- analyze_boot_splines(bootstrapped_familiar)## Warning: Calling boot-splines from this function is deprecated. Please use
## `analyze_time_bins` for boot-splines.summary(bootstrap_analysis_familiar)## Divergences:
## 1: 170 - 5330Cluster Analysis
alpha = .20
num_sub = length(unique((familiar$ParticipantName)))
threshold_t = qt(p = 1 - alpha/2,
df = num_sub-1)
df_timeclust <- make_time_cluster_data(response_familiar_time,
test= "t.test",
paired = TRUE,
predictor_column = "Target",
threshold = threshold_t,
formula = ArcSin ~ Target)
plot(df_timeclust) +
ylab("T-Statistic")
summary(df_timeclust)## Test Type: t.test
## Predictor: Target
## Formula: ArcSin ~ Target
## Summary of Clusters ======
## Cluster Direction SumStatistic StartTime EndTime
## 1 1 Positive 1823.7147 200 4440
## 2 2 Positive 147.3181 4460 5360clust_analysis <- analyze_time_clusters(df_timeclust, within_subj=TRUE, paired=TRUE, samples=5000, parallel=TRUE)
clust_analysis## Test Type: t.test
## Predictor: Target
## Formula: ArcSin ~ Target
## Null Distribution ======
## Mean: -0.1405
## 2.5%: -176.2248
## 97.5%: 185.4092
## Summary of Clusters ======
## Cluster Direction SumStatistic StartTime EndTime Probability
## 1 1 Positive 1823.7147 200 4440 0.000
## 2 2 Positive 147.3181 4460 5360 0.076plot(clust_analysis)
Onset-contingent Analysis
# recode AOIs to target & distractor
familiar$TrialTarget <- ifelse(familiar$Target == 'Animate', familiar$Animate, familiar$Inanimate)
familiar$TrialDistractor <- ifelse(familiar$Target == 'Animate', familiar$Inanimate, familiar$Animate)
onsets <- make_onset_data(familiar, onset_time = 0,
fixation_window_length = 100, target_aoi='TrialTarget')## Warning in make_onset_data(familiar, onset_time = 0, fixation_window_length = 100, : Smoothing in make_onset_data() using fixation_window_length_rows is experimental. We
## recommend looking closely at the output to validate it.# participants' ability to orient to the trial target overall:
plot(onsets) + theme(legend.text=element_text(size=5))
onset_switches <- make_switch_data(onsets, predictor_columns = c("Target","TrialNumber"))
# visualize subject's switch times
plot(onset_switches)## Warning: Removed 21 rows containing non-finite values (stat_boxplot).## Warning: Removed 21 rows containing missing values (geom_point).
# center predictor:
onset_switches$FirstAOIC <- ifelse(onset_switches$FirstAOI == 'TrialTarget', .5, -.5)
onset_switches$FirstAOIC <- onset_switches$FirstAOIC - mean(onset_switches$FirstAOIC, na.rm=TRUE)
onset_switches$TargetC <- ifelse(onset_switches$Target == 'Animate', .5, -.5)
onset_switches$TargetC <- onset_switches$TargetC - mean(onset_switches$TargetC)
onset_switches$TrialNumberC <- onset_switches$TrialNumber - mean(onset_switches$TrialNumber)
# build model:
model_switches <- lmer(FirstSwitch ~ (FirstAOIC+TargetC+TrialNumberC)^2 + (1 | Trial) + (1 + TargetC | ParticipantName), data=onset_switches, REML=FALSE)
summary(model_switches)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## FirstSwitch ~ (FirstAOIC + TargetC + TrialNumberC)^2 + (1 | Trial) +
## (1 + TargetC | ParticipantName)
## Data: onset_switches
##
## AIC BIC logLik deviance df.resid
## 3737.4 3778.3 -1856.7 3713.4 211
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9190 -0.5383 -0.1554 0.4629 4.0785
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantName (Intercept) 128394 358.3
## TargetC 118015 343.5 1.00
## Trial (Intercept) 15464 124.4
## Residual 874740 935.3
## Number of obs: 223, groups: ParticipantName, 48; Trial, 6
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1268.816 107.676 11.784
## FirstAOIC 1578.763 148.692 10.618
## TargetC 486.504 210.195 2.315
## TrialNumberC 128.042 54.412 2.353
## FirstAOIC:TargetC 1496.848 330.901 4.524
## FirstAOIC:TrialNumberC 2.122 108.680 0.020
## TargetC:TrialNumberC 204.435 128.268 1.594
##
## Correlation of Fixed Effects:
## (Intr) FrAOIC TargtC TrlNmC FAOIC:TC FAOIC:TN
## FirstAOIC -0.016
## TargetC 0.010 -0.247
## TrialNumbrC 0.008 -0.023 -0.319
## FrstAOIC:TC -0.272 0.127 0.146 0.100
## FrsAOIC:TNC 0.129 0.009 -0.022 -0.131 -0.391
## TrgtC:TrlNC -0.330 0.038 0.224 0.097 0.143 -0.487drop1(model_switches,~.,test="Chi")## Single term deletions
##
## Model:
## FirstSwitch ~ (FirstAOIC + TargetC + TrialNumberC)^2 + (1 | Trial) +
## (1 + TargetC | ParticipantName)
## Df AIC LRT Pr(Chi)
## <none> 3737.4
## FirstAOIC 1 3818.9 83.454 < 2.2e-16 ***
## TargetC 1 3739.3 3.855 0.04959 *
## TrialNumberC 1 3739.5 4.128 0.04218 *
## FirstAOIC:TargetC 1 3754.6 19.215 1.168e-05 ***
## FirstAOIC:TrialNumberC 1 3735.4 0.000 0.98618
## TargetC:TrialNumberC 1 3737.9 2.465 0.11644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Unfamiliar Trials
unfamiliar_raw <- filter(response_window, TrialType == 'Unfamiliar')Clean by trackloss
trackloss <- trackloss_analysis(data = unfamiliar_raw)
# set a threshold for amount of trackloss that is acceptable
prop_thresh <- mean(trackloss$TracklossForTrial) + 2*sd(trackloss$TracklossForTrial)
unfamiliar <- clean_by_trackloss(data = unfamiliar_raw, trial_prop_thresh = prop_thresh)## Performing Trackloss Analysis...## Will exclude trials whose trackloss proportion is greater than : 0.705477220469878## ...removed 16 trials.trackloss_clean <- trackloss_analysis(data = unfamiliar)
trackloss_clean_subjects <- unique(trackloss_clean[, c('ParticipantName','TracklossForParticipant')])
# get mean samples contributed per trials, with SD
mean(1 - trackloss_clean_subjects$TracklossForParticipant)## [1] 0.8094455sd(1- trackloss_clean_subjects$TracklossForParticipant)## [1] 0.1312352# get number of trials contributed by participant
interim_summary <- describe_data(unfamiliar, 'Animate', c('ParticipantName'))
interim_summary## Source: local data frame [48 x 8]
##
## ParticipantName Mean SD Var Min Max N
## <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 ANCAT177 0.6269336 0.4838397 0.23410088 0 1 1980
## 2 ANCAT180 0.7254733 0.4464676 0.19933332 0 1 1980
## 3 ANCAT181 0.4805492 0.4997645 0.24976455 0 1 1980
## 4 ANCAT183 0.2767857 0.4478096 0.20053348 0 1 1320
## 5 ANCAT184 0.6773802 0.4676204 0.21866886 0 1 1980
## 6 ANCAT190 0.6974044 0.4595386 0.21117576 0 1 1980
## 7 ANCAT191 0.5756894 0.4943770 0.24440866 0 1 1980
## 8 ANCAT194 0.3730018 0.4838175 0.23407934 0 1 1650
## 9 ANCAT195 0.5646863 0.4959357 0.24595225 0 1 1980
## 10 ANCAT198 0.9373454 0.2424406 0.05877744 0 1 1650
## .. ... ... ... ... ... ... ...
## Variables not shown: NumTrials <int>.# remove participants who have less than 3 trials of either type
subjects_to_remove <- as.character(filter(interim_summary, NumTrials < 3)$ParticipantName)
unfamiliar <- unfamiliar %>%
filter(!(ParticipantName %in% subjects_to_remove))
# final counts:
final_summary <- describe_data(unfamiliar, 'Animate', c('ParticipantName'))
final_summary## Source: local data frame [48 x 8]
##
## ParticipantName Mean SD Var Min Max N
## <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 ANCAT177 0.6269336 0.4838397 0.23410088 0 1 1980
## 2 ANCAT180 0.7254733 0.4464676 0.19933332 0 1 1980
## 3 ANCAT181 0.4805492 0.4997645 0.24976455 0 1 1980
## 4 ANCAT183 0.2767857 0.4478096 0.20053348 0 1 1320
## 5 ANCAT184 0.6773802 0.4676204 0.21866886 0 1 1980
## 6 ANCAT190 0.6974044 0.4595386 0.21117576 0 1 1980
## 7 ANCAT191 0.5756894 0.4943770 0.24440866 0 1 1980
## 8 ANCAT194 0.3730018 0.4838175 0.23407934 0 1 1650
## 9 ANCAT195 0.5646863 0.4959357 0.24595225 0 1 1980
## 10 ANCAT198 0.9373454 0.2424406 0.05877744 0 1 1650
## .. ... ... ... ... ... ... ...
## Variables not shown: NumTrials <int>.# how many participants?
length(unique(final_summary$ParticipantName))## [1] 48# how many trials?
mean(final_summary$NumTrials)## [1] 5.666667sd(final_summary$NumTrials)## [1] 0.6631111Summarize
(unfamiliar_summary <- unfamiliar %>%
group_by(ParticipantName, Target) %>%
summarise(Animate = mean(Animate, na.rm=TRUE),
NTrials = length(unique(Trial))) %>%
ungroup() %>%
group_by(Target) %>%
summarise(MeanAnimate = mean(Animate),
SDAnimate = sd(Animate),
N = n(),
MeanTrials = mean(NTrials),
SDTrials = sd(NTrials)) %>%
ungroup())## Source: local data frame [1 x 6]
##
## Target MeanAnimate SDAnimate N MeanTrials SDTrials
## <fctr> <dbl> <dbl> <int> <dbl> <dbl>
## 1 Animate 0.5821864 0.1442839 48 5.666667 0.6631111subjects <- unfamiliar %>%
distinct(ParticipantName,Age_Group,Age,Sex,Condition) %>%
group_by(Condition,Age_Group) %>%
summarise(N=length(ParticipantName), MeanAge = mean(Age,na.rm=TRUE), N_Males = length(Sex[Sex=='M'])) %>%
ungroup()
subjects## Source: local data frame [2 x 5]
##
## Condition Age_Group N MeanAge N_Males
## <fctr> <fctr> <int> <dbl> <int>
## 1 Informative 24-months 24 24.13667 11
## 2 Neutral 24-months 24 24.22208 12Window Analysis
unfamiliar_summary <- describe_data(unfamiliar, describe_column='Animate', group_columns=c('Condition'))
unfamiliar_summary## Source: local data frame [2 x 8]
##
## Condition Mean SD Var Min Max N NumTrials
## <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 Informative 0.6322832 0.4821904 0.2325075 0 1 45210 6
## 2 Neutral 0.5536108 0.4971243 0.2471326 0 1 44550 6# agg by subject
unfamiliar_sub <- make_time_window_data(unfamiliar,
aois='Animate',
predictor_columns=c('Condition','Age','MCDI_Total'),
summarize_by = "ParticipantName")
# take a quick peek at data
ggplot(unfamiliar_sub, aes(x=Condition,y=Prop)) +
geom_boxplot() +
geom_point()
# simple paired t-test between conditions
t.test(ArcSin ~ Condition, data=unfamiliar_sub, var.equal=T)##
## Two Sample t-test
##
## data: ArcSin by Condition
## t = 2.3296, df = 46, p-value = 0.02427
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01354537 0.18573446
## sample estimates:
## mean in group Informative mean in group Neutral
## 0.9225841 0.8229442# mixed-effects model
unfamiliar_trial <- make_time_window_data(unfamiliar,
aois='Animate',
predictor_columns=c('Condition','Age'))
# sum-code and center predictors
unfamiliar_trial$ConditionC <- ifelse(unfamiliar_trial$Condition == 'Informative', .5, -.5)
unfamiliar_trial$ConditionC <- unfamiliar_trial$ConditionC - mean(unfamiliar_trial$ConditionC)
unfamiliar_trial$AgeC <- unfamiliar_trial$Age - mean(unfamiliar_trial$Age,na.rm=T)
# mixed-effects linear model on subject*trial data
model <- lmer(ArcSin ~ ConditionC + (1 | Trial) + (1 | ParticipantName), data = unfamiliar_trial, REML = FALSE)
summary(model)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ArcSin ~ ConditionC + (1 | Trial) + (1 | ParticipantName)
## Data: unfamiliar_trial
##
## AIC BIC logLik deviance df.resid
## 146.1 164.1 -68.0 136.1 267
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.61248 -0.56946 -0.05782 0.56935 2.56757
##
## Random effects:
## Groups Name Variance Std.Dev.
## ParticipantName (Intercept) 0.011200 0.10583
## Trial (Intercept) 0.008665 0.09309
## Residual 0.084348 0.29043
## Number of obs: 272, groups: ParticipantName, 48; Trial, 6
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.90420 0.04462 20.266
## ConditionC 0.10516 0.04672 2.251
##
## Correlation of Fixed Effects:
## (Intr)
## ConditionC 0.001drop1(model,~.,test="Chi")## Single term deletions
##
## Model:
## ArcSin ~ ConditionC + (1 | Trial) + (1 | ParticipantName)
## Df AIC LRT Pr(Chi)
## <none> 146.10
## ConditionC 1 148.93 4.8349 0.02789 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Growth Curve Analysis
# aggregate across trials within subjects in time analysis
response_time <- make_time_sequence_data(unfamiliar, time_bin_size = 100,
predictor_columns = c("Condition","Age_Group"),
aois = "Animate"
)
# visualize time results
plot(response_time, predictor_column = "Condition") +
theme_light() +
coord_cartesian(ylim = c(0,1))## Warning: Removed 7 rows containing non-finite values (stat_summary).
## Warning: Removed 7 rows containing non-finite values (stat_summary).
response_time_sub <- response_time %>%
group_by(ParticipantName,Condition,Age_Group,Time) %>%
summarise(Prop=mean(Prop,na.rm=TRUE)) %>%
ungroup()
ggplot(response_time_sub, aes(x=Time, y=Prop, color=Condition, fill=Condition)) +
stat_summary(fun.y='mean', geom='line') +
stat_summary(fun.data='mean_cl_normal', geom='ribbon', alpha=.3, color=NA, fun.args=list(mult=1)) +
facet_grid(~Age_Group)## Warning: Removed 7 rows containing non-finite values (stat_summary).
## Warning: Removed 7 rows containing non-finite values (stat_summary).
# sum-code and center our predictor:
response_time$ConditionC <- ifelse(response_time$Condition == 'Informative', .5, -.5)
response_time$ConditionC <- response_time$ConditionC - mean(response_time$ConditionC)
# Construct model
model_time_sequence <- lmer(ArcSin ~ ConditionC*(ot1+ot2+ot3+ot4) + (1 + ot1 + ot2 + ot3 + ot4 | Trial) + (1 + ot1 + ot2 + ot3 + ot4 | ParticipantName), data = response_time, REML = FALSE)
summary(model_time_sequence)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ArcSin ~ ConditionC * (ot1 + ot2 + ot3 + ot4) + (1 + ot1 + ot2 +
## ot3 + ot4 | Trial) + (1 + ot1 + ot2 + ot3 + ot4 | ParticipantName)
## Data: response_time
##
## AIC BIC logLik deviance df.resid
## 26893.8 27200.1 -13405.9 26811.8 12915
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0072 -0.8906 0.2090 0.8247 2.4119
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ParticipantName (Intercept) 0.03746 0.1936
## ot1 1.39348 1.1805 -0.15
## ot2 1.04744 1.0234 -0.23 0.32
## ot3 0.77615 0.8810 -0.31 -0.39 -0.06
## ot4 0.70943 0.8423 -0.07 -0.26 -0.64 0.15
## Trial (Intercept) 0.01680 0.1296
## ot1 0.11053 0.3325 -0.12
## ot2 0.08356 0.2891 0.68 -0.71
## ot3 0.11875 0.3446 0.38 -0.55 0.31
## ot4 0.12691 0.3562 0.48 -0.53 0.70 0.12
## Residual 0.44180 0.6647
## Number of obs: 12956, groups: ParticipantName, 48; Trial, 6
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.90988 0.06016 15.126
## ConditionC 0.11494 0.05721 2.009
## ot1 -0.66152 0.22274 -2.970
## ot2 0.03497 0.19464 0.180
## ot3 0.33796 0.19503 1.733
## ot4 0.04879 0.19490 0.250
## ConditionC:ot1 -0.48622 0.35312 -1.377
## ConditionC:ot2 -0.08882 0.30952 -0.287
## ConditionC:ot3 0.38461 0.27006 1.424
## ConditionC:ot4 -0.13991 0.25937 -0.539
##
## Correlation of Fixed Effects:
## (Intr) CndtnC ot1 ot2 ot3 ot4 CndC:1 CndC:2 CndC:3
## ConditionC 0.004
## ot1 -0.116 0.001
## ot2 0.284 0.000 -0.070
## ot3 0.148 -0.002 -0.432 0.114
## ot4 0.294 0.000 -0.369 0.014 0.132
## ConditnC:t1 0.001 -0.134 0.007 0.003 -0.001 -0.001
## ConditnC:t2 0.000 -0.214 0.003 0.007 0.002 -0.001 0.306
## ConditnC:t3 -0.001 -0.289 -0.001 0.003 0.007 0.003 -0.348 -0.041
## ConditnC:t4 0.000 -0.061 -0.001 -0.002 0.003 0.006 -0.239 -0.574 0.148plot(response_time, predictor_column = "Condition", dv = "ArcSin", model = model_time_sequence) + theme_light()## Warning: Removed 7 rows containing non-finite values (stat_summary).
## Warning: Removed 7 rows containing non-finite values (stat_summary).
Bootstrapped Spline Analysis
response_unfamiliar_time <- make_time_sequence_data(unfamiliar,
time_bin_size = 10,
predictor_columns = c("Condition","Age_Group"),
aois = "Animate",
summarize_by = "ParticipantName"
)
# visualize timecourse
plot(response_unfamiliar_time, predictor_column = "Condition") +
theme_light() +
coord_cartesian(ylim = c(0,1))## Warning: Removed 109 rows containing non-finite values (stat_summary).
## Warning: Removed 109 rows containing non-finite values (stat_summary).
# get rid of NA's so we can use splines
response_unfamiliar_time_nonas <- response_unfamiliar_time[!is.na(response_unfamiliar_time$Prop), ]
# bootstrap with smoothing splines
bootstrapped_unfamiliar <- make_boot_splines_data(response_unfamiliar_time_nonas,
predictor_column = 'Condition',
within_subj = FALSE,
bs_samples = 5000,
alpha = .05,
smoother = "smooth.spline")## Warning: Calling boot-splines from this function is deprecated. Please use
## `analyze_time_bins` for boot-splines.plot(bootstrapped_unfamiliar)
# show us the divergences where p < .05
bootstrap_analysis_unfamiliar <- analyze_boot_splines(bootstrapped_unfamiliar)## Warning: Calling boot-splines from this function is deprecated. Please use
## `analyze_time_bins` for boot-splines.summary(bootstrap_analysis_unfamiliar)## Divergences:
## 1: 920 - 1940Cluster Analysis
alpha = .20
num_sub = length(unique((response_unfamiliar_time$ParticipantName)))
threshold_t = qt(p = 1 - alpha/2,
df = num_sub-1)
# differences in positive direction...
df_timeclust <- make_time_cluster_data(response_unfamiliar_time,
test= "t.test",
paired = FALSE,
predictor_column = "Condition",
threshold = threshold_t,
formula = ArcSin ~ Condition)
plot(df_timeclust) +
ylab("T-Statistic")
summary(df_timeclust)## Test Type: t.test
## Predictor: Condition
## Formula: ArcSin ~ Condition
## Summary of Clusters ======
## Cluster Direction SumStatistic StartTime EndTime
## 1 1 Positive 4.002310 0 20
## 2 2 Positive 2.766681 110 140
## 3 3 Positive 1.299990 160 170
## 4 4 Positive 1.324528 430 440
## 5 5 Positive 1.377453 480 490
## 6 6 Positive 19.538362 550 720
## 7 7 Positive 158.974954 760 2020
## 8 8 Positive 7.876571 2050 2120
## 9 9 Positive 2.997240 2330 2360
## 10 10 Positive 5.915391 2430 2490
## 11 11 Positive 4.402373 2510 2560
## 12 12 Positive 14.382520 2580 2710
## 13 13 Positive 1.369323 3150 3160
## 14 14 Positive 4.312072 4510 4560
## 15 15 Positive 5.147612 4580 4620
## 16 16 Positive 3.612785 4760 4790
## 17 17 Positive 1.408695 4810 4820
## 18 18 Positive 1.336772 4850 4860
## 19 19 Positive 3.040730 4960 4990
## 20 20 Negative -9.604390 3780 3870
## 21 21 Negative -1.720093 3930 3940
## 22 22 Negative -2.837912 3960 3990
## 23 23 Negative -1.332187 5430 5440
## 24 24 Negative -1.353890 5460 5470clust_analysis <- analyze_time_clusters(df_timeclust, within_subj=FALSE, paired=FALSE, samples=5000, parallel=TRUE)
clust_analysis## Test Type: t.test
## Predictor: Condition
## Formula: ArcSin ~ Condition
## Null Distribution ======
## Mean: 0.2779
## 2.5%: -148.1146
## 97.5%: 149.6981
## Summary of Clusters ======
## Cluster Direction SumStatistic StartTime EndTime Probability
## 1 1 Positive 4.002310 0 20 0.9766
## 2 2 Positive 2.766681 110 140 0.9916
## 3 3 Positive 1.299990 160 170 0.9994
## 4 4 Positive 1.324528 430 440 0.9992
## 5 5 Positive 1.377453 480 490 0.9986
## 6 6 Positive 19.538362 550 720 0.6908
## 7 7 Positive 158.974954 760 2020 0.0400
## 8 8 Positive 7.876571 2050 2120 0.9056
## 9 9 Positive 2.997240 2330 2360 0.9872
## 10 10 Positive 5.915391 2430 2490 0.9454
## 11 11 Positive 4.402373 2510 2560 0.9690
## 12 12 Positive 14.382520 2580 2710 0.7790
## 13 13 Positive 1.369323 3150 3160 0.9986
## 14 14 Positive 4.312072 4510 4560 0.9708
## 15 15 Positive 5.147612 4580 4620 0.9544
## 16 16 Positive 3.612785 4760 4790 0.9782
## 17 17 Positive 1.408695 4810 4820 0.9980
## 18 18 Positive 1.336772 4850 4860 0.9992
## 19 19 Positive 3.040730 4960 4990 0.9862
## 20 20 Negative -9.604390 3780 3870 0.8740
## 21 21 Negative -1.720093 3930 3940 0.9934
## 22 22 Negative -2.837912 3960 3990 0.9904
## 23 23 Negative -1.332187 5430 5440 0.9992
## 24 24 Negative -1.353890 5460 5470 0.9990plot(clust_analysis)
Onset-contingent Analysis
onsets <- make_onset_data(unfamiliar, onset_time = 0,
fixation_window_length = 100, target_aoi='Animate')## Warning in make_onset_data(unfamiliar, onset_time = 0, fixation_window_length = 100, : Smoothing in make_onset_data() using fixation_window_length_rows is experimental. We
## recommend looking closely at the output to validate it.# participants' ability to orient to the trial target overall:
plot(onsets, predictor_columns = "Condition") + theme(legend.text=element_text(size=5))
onset_switches <- make_switch_data(onsets, predictor_columns = c("Condition","TrialNumber"))
# visualize subject's switch times
plot(onset_switches, predictor_columns = "Condition")## Warning: Removed 18 rows containing non-finite values (stat_boxplot).## Warning: Removed 18 rows containing missing values (geom_point).
# center predictor:
onset_switches$FirstAOIC <- ifelse(onset_switches$FirstAOI == 'Animate', .5, -.5)
onset_switches$FirstAOIC <- onset_switches$FirstAOIC - mean(onset_switches$FirstAOIC, na.rm=TRUE)
onset_switches$ConditionC <- ifelse(onset_switches$Condition == 'Informative', .5, -.5)
onset_switches$ConditionC <- onset_switches$ConditionC - mean(onset_switches$ConditionC)
onset_switches$TrialNumberC <- onset_switches$TrialNumber - mean(onset_switches$TrialNumber)
# build model:
model_switches <- lmer(FirstSwitch ~ (FirstAOIC+ConditionC+TrialNumberC)^2 + (1 | Trial) + (1 | ParticipantName), data=onset_switches, REML=FALSE)
summary(model_switches)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: FirstSwitch ~ (FirstAOIC + ConditionC + TrialNumberC)^2 + (1 |
## Trial) + (1 | ParticipantName)
## Data: onset_switches
##
## AIC BIC logLik deviance df.resid
## 4069.4 4104.0 -2024.7 4049.4 226
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6664 -0.6696 -0.2483 0.4656 3.1635
##
## Random effects:
## Groups Name Variance Std.Dev.
## ParticipantName (Intercept) 2.603e+05 5.102e+02
## Trial (Intercept) 5.212e-11 7.220e-06
## Residual 1.460e+06 1.208e+03
## Number of obs: 236, groups: ParticipantName, 48; Trial, 6
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1523.43 109.20 13.951
## FirstAOIC 677.97 169.29 4.005
## ConditionC -150.68 218.37 -0.690
## TrialNumberC 38.90 48.23 0.807
## FirstAOIC:ConditionC 472.28 338.67 1.395
## FirstAOIC:TrialNumberC 162.53 107.72 1.509
## ConditionC:TrialNumberC -60.93 96.18 -0.633
##
## Correlation of Fixed Effects:
## (Intr) FrAOIC CndtnC TrlNmC FAOIC:C FAOIC:T
## FirstAOIC 0.047
## ConditionC -0.004 -0.079
## TrialNumbrC 0.021 0.035 0.047
## FrstAOIC:CC -0.078 0.047 0.048 -0.004
## FrsAOIC:TNC 0.018 -0.011 -0.004 -0.124 0.035
## CndtnC:TrNC 0.045 0.001 0.023 0.023 0.030 -0.098drop1(model_switches,~.,test="Chi")## Single term deletions
##
## Model:
## FirstSwitch ~ (FirstAOIC + ConditionC + TrialNumberC)^2 + (1 |
## Trial) + (1 | ParticipantName)
## Df AIC LRT Pr(Chi)
## <none> 4069.4
## FirstAOIC 1 4082.9 15.4979 8.26e-05 ***
## ConditionC 1 4067.8 0.4704 0.4928
## TrialNumberC 1 4068.0 0.6482 0.4208
## FirstAOIC:ConditionC 1 4069.3 1.9327 0.1645
## FirstAOIC:TrialNumberC 1 4069.6 2.2640 0.1324
## ConditionC:TrialNumberC 1 4067.8 0.4010 0.5266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# within Informative, to look for speed-up over trials:
model_switches_inf <- lmer(FirstSwitch ~ (FirstAOIC+TrialNumberC)^2 + (1 | Trial) + (1 | ParticipantName), data=onset_switches %>% filter(Condition == 'Informative'), REML=FALSE)
summary(model_switches_inf)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: FirstSwitch ~ (FirstAOIC + TrialNumberC)^2 + (1 | Trial) + (1 |
## ParticipantName)
## Data: onset_switches %>% filter(Condition == "Informative")
##
## AIC BIC logLik deviance df.resid
## 2061.9 2081.4 -1023.9 2047.9 113
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6604 -0.6678 -0.2250 0.3166 3.2937
##
## Random effects:
## Groups Name Variance Std.Dev.
## ParticipantName (Intercept) 167598 409.4
## Trial (Intercept) 0 0.0
## Residual 1374153 1172.2
## Number of obs: 120, groups: ParticipantName, 24; Trial, 6
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1440.435 137.363 10.486
## FirstAOIC 928.657 234.293 3.964
## TrialNumberC 8.316 67.433 0.123
## FirstAOIC:TrialNumberC 151.426 152.923 0.990
##
## Correlation of Fixed Effects:
## (Intr) FrAOIC TrlNmC
## FirstAOIC -0.033
## TrialNumbrC 0.068 0.025
## FrsAOIC:TNC 0.015 0.033 -0.225drop1(model_switches_inf,~.,test="Chi")## Single term deletions
##
## Model:
## FirstSwitch ~ (FirstAOIC + TrialNumberC)^2 + (1 | Trial) + (1 |
## ParticipantName)
## Df AIC LRT Pr(Chi)
## <none> 2061.9
## FirstAOIC 1 2074.5 14.5976 0.0001331 ***
## TrialNumberC 1 2059.9 0.0152 0.9019056
## FirstAOIC:TrialNumberC 1 2060.8 0.9695 0.3247973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(onset_switches %>%
filter(Condition == 'Informative') %>%
mutate(Half = ifelse(TrialNumber <= 9, 'First', 'Last')) %>%
group_by(ParticipantName,Half,FirstAOI) %>%
summarise(
FirstSwitch = mean(FirstSwitch,na.rm=T)
) %>%
group_by(Half,FirstAOI) %>%
summarise(
M = mean(FirstSwitch,na.rm=T),
SD = sd(FirstSwitch,na.rm=T)
))## Source: local data frame [6 x 4]
## Groups: Half [?]
##
## Half FirstAOI M SD
## (chr) (fctr) (dbl) (dbl)
## 1 First Animate 1578.9048 1017.4942
## 2 First Not_Animate 1065.0278 1359.5333
## 3 First NA NaN NA
## 4 Last Animate 1960.6905 1318.1607
## 5 Last Not_Animate 543.5882 416.9676
## 6 Last NA NaN NaN```